Analysis of Multi-Criteria Fire Detection Data and Early Warning Fire Detection Prototype Selection
Analysis of Multi-Criteria Fire Detection Data and Early Warning Fire Detection Prototype Selection

Author:

Publisher:

Published: 2000

Total Pages: 30

ISBN-13:

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This report describes the analysis of Fire/Nuisance Source data and the selection of sensors for an early warning, multi-criteria, fire detection system for the Office of Naval Research (ONR) program on Damage Control: Automation for Reduced Manning (DC-ARM). In this work, the analysis of transient fire signatures is studied using a probabilistic neural network (PNN). Experiments are described to study the effects of various PNN training parameters and to determine the optimal sensor suite combination, which enables both early fire detection and high nuisance source rejection. Comparisons are made between the candidate sensor arrays, commercial fire detection systems, and sensor arrays proposed in previous reports Recommendations and directions for future research are also given.

Development of an Early Warning Multi-criteria Fire Detection System: Analysis of Transient Fire Signatures Using a Probabilistic Neural Network
Development of an Early Warning Multi-criteria Fire Detection System: Analysis of Transient Fire Signatures Using a Probabilistic Neural Network

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Publisher:

Published: 2000

Total Pages: 33

ISBN-13:

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This report describes the progress made in developing an early warning, multi-criteria, fire detection system for the Office of Naval Research (ONR) program on Damage Control: Automation for Reduced Manning (DC-ARM). In this work, the analysis of transient fire signatures is studied using a probabilistic neural network (PNN). Experiments are described to study the effects of various PNN training parameters and to determine the optimal sensor suite combination, which enables both early fire detection and high nuisance source rejection. Comparisons are made between the candidate sensor arrays, commercial fire detection systems, and sensor arrays proposed in previous reports. Recommendations and directions for future research are also given.

Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 1 Results
Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 1 Results

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Published: 2000

Total Pages: 0

ISBN-13:

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A series of tests were conducted to evaluate and improve the multivariate data analysis methods and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer nuisance alarms than commercially available smoke detection systems. Tests were conducted from 7-18 February 2000, onboard the ex-USS Sizadwell. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm yielded performance gains over the real-time results. Simulation studies have been done to examine the effects of sensor drop-out, excessive noise, and erroneous sensor values.

Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results
Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results

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Published: 2000

Total Pages: 0

ISBN-13:

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A second series of tests was conducted to evaluate and improve the multivariate data analysis notebooks and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer nuisance alarms than commercially available smoke detection systems. Tests were conducted from 25 April to 5 May 2000 onboard the ex-USS SHADWELL. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm has yielded performance gains over the real-time results. Modifications have been made that improve the real-time data acquisition and the ion sensor calibration. Background subtraction was investigated and will be used in future tests. The best performance was provided by a four sensor array consisting of ionization, photoelectric carbon monoxide and carbon dioxide sensors.

Army RD & A.
Army RD & A.

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Publisher:

Published: 1999

Total Pages: 548

ISBN-13:

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Artificial Intelligence, Computer and Software Engineering Advances
Artificial Intelligence, Computer and Software Engineering Advances

Author: Miguel Botto-Tobar

Publisher: Springer Nature

Published: 2021-04-20

Total Pages: 489

ISBN-13: 3030680800

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This book constitutes the proceedings of the XV Multidisciplinary International Congress on Science and Technology (CIT 2020), held in Quito, Ecuador, on 26–30 October 2020, proudly organized by Universidad de las Fuerzas Armadas ESPE in collaboration with GDEON. CIT is an international event with a multidisciplinary approach that promotes the dissemination of advances in Science and Technology research through the presentation of keynote conferences. In CIT, theoretical, technical, or application works that are research products are presented to discuss and debate ideas, experiences, and challenges. Presenting high-quality, peer-reviewed papers, the book discusses the following topics: Artificial Intelligence Computational Modeling Data Communications Defense Engineering Innovation, Technology, and Society Managing Technology & Sustained Innovation, and Business Development Modern Vehicle Technology Security and Cryptography Software Engineering